Device, system and method for determining compliance with a positioning instruction by a figure in an image

ABSTRACT

A device, system and method for calculating location coordinates for a figure in an image that is illuminated by visible light, comparing such location coordinates to location coordinates of a figure, and evaluating compliance by such figure to an instruction to assume a defined position.

CROSS REFERENCE TO RELATED APPLICATION

This application is a continuation of U.S. patent application Ser. No.11/389,310 filed on Mar. 27, 2006, now U.S. Pat. No. 7,515,734, entitled“DEVICE, SYSTEM AND METHOD FOR DETERMINING COMPLIANCE WITH A POSITIONINGINSTRUCTION BY A FIGURE IN AN IMAGE” which is incorporated in itsentirety herein by reference.

FIELD OF THE INVENTION

The present invention generally relates to image processing, andparticularly to evaluating a change in a position of a figure in animage.

BACKGROUND OF THE INVENTION

Various activities require that a user or subject assume a firstposition, and then in response to an instruction, assume a secondposition. For example, security confirmation procedures may require auser to first face a camera, and then look away from a camera. Sports,exercise programs, training courses or physical therapy regimens mayrequire a user or subject to assume one or more positions in the courseof a session, and to then change such positions in response to forexample one or more instructions. Currently, a trained observerinstructs a user or subject to change a position, and then evaluates orcorrects the subject's compliance with the instruction by observing thechanged position.

SUMMARY OF THE INVENTION

Embodiments of the invention include a system having an imaging deviceto capture an image of a figure using visible light, a processor and anoutput device, where the imaging device is to capture an image of afigure in a position and the processor is to determine compliance withan instruction to change the position of the figure. In someembodiments, the output device is to issue an indication of compliancewith the instruction. In some embodiments, the output device is to issuean instruction to change a position of the figure to another position,and the processor is to determine compliance with such instruction. Insome embodiments, the processor is to assess a non-compliance with theinstruction, and the output device is to issue a suggestion to correctthe non-compliance. In some embodiments, the processor is to identify acolor of an object in the image. In some embodiments, the processor isto differentiate the figure in the image from background image data. Insome embodiments, the processor is to calculate a coordinate of askeleton of the figure. In some embodiments, the processor is tocalculate an intersection of a head-torso axis and a shoulder axis ofthe figure. In some embodiments, the processor is to calculate a convexdeficiency of a skeleton of the figure. In some embodiments, theprocessor is to calculate an openness of the convex deficiency. In someembodiments, the processor is to calculate a distance and position of acenter of mass of a convex deficiency from a center of mass of askeleton of the figure. In some embodiments, the processor is toidentify a shape of an object in the image. In some embodiments, theprocessor is to cluster a color in the image. In some embodiments, theimaging device is to capture another image of the figure; and theprocessor is to calculate a distance and position of a center of mass ofa convex deficiency from a center of mass of a skeleton of the figure inthe image, and is to calculate a distance and position of a center ofmass of a convex deficiency from a center of mass of a skeleton of thefigure in the another image, and is to compare the distance and positionof the figure in the image to the distance and position of the figure inthe another image.

In some embodiments the invention comprises a device having a memory, aprocessor, an output device and an imager, where an image is capturedusing visible light and where the processor calculates two or more firstlocation coordinates of a figure in an image; the output device issues asignal to alter a position of the figure, and the processor compares thetwo or more first location coordinates stored in the memory to two ormore second location coordinates, and the output device issues a signalof a result of the comparison. In some embodiments, the two or more ofthe first location coordinates correspond to two or more mass centerpoints of convex deficiencies of a skeleton of the figure. In someembodiments, the processor is to calculate two or more of clusters ofcolors in the image, and the processor it to compare the two or moreclusters to color cluster information stored in the memory.

Some embodiments include a method of calculating a position coordinateof a figure in a first image that is captured using visible light,comparing the position coordinate of the figure in the first image to aposition coordinate of the figure in a second image, and evaluatingcompliance by the figure in the second image with an instruction toassume a defined position. Some embodiments include calculating adistance and position of a mass center of a convex hull deficiency inthe second image from a mass center of the figure in the second image.Some embodiments include clustering color pixels in the first image, andcomparing clustered color pixels in the first image to color clusters inthe second image.

BRIEF DESCRIPTION OF THE DRAWINGS

The subject matter regarded as the invention is particularly pointed outand distinctly claimed in the concluding portion of the specification.The invention, however, both as to organization and method of operation,together with features and advantages thereof, may best be understood byreference to the following detailed description when read with theaccompanied drawings in which:

FIG. 1 is a conceptual illustration of components of a system, inaccordance with a preferred embodiment of the present invention;

FIG. 2A illustrates a binary filtered image of a figure in accordancewith an embodiment of the invention;

FIG. 2B illustrates a skeletonized image of a figure in accordance withan embodiment of the invention;

FIG. 2C illustrates convex hull deficiencies of the skeletonized imageof a figure in accordance with an embodiment of the invention;

FIG. 2D illustrates distances and positions of centers of mass of convexhull deficiencies from a center of mass of a figure, in accordance withan embodiment of the invention;

FIG. 2E illustrates a proportion of convex hull deficiency perimetersthat are included in a skeleton of a figure in accordance with anembodiment of the invention; and

FIG. 3 is a flow diagram of a method in accordance with an embodiment ofthe invention.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

In the following description, various embodiments of the invention willbe described. For purposes of explanation, specific examples are setforth in order to provide a thorough understanding of at least oneembodiment of the invention. However, it will also be apparent to oneskilled in the art that other embodiments of the invention are notlimited to the examples described herein. Furthermore, well-knownfeatures or processes may be omitted or simplified in order not toobscure embodiments of the invention described herein.

Unless specifically stated otherwise, as apparent from the followingdiscussions, it is appreciated that throughout the specification,discussions utilizing terms such as “selecting,” “processing,”“computing,” “calculating,” “determining,” “designating,” “allocating”or the like, refer to the actions and/or processes of a computer,computer processor or computing system, or similar electronic computingdevice, that manipulate and/or transform data such as for example imagedata represented as physical, such as electronic, quantities within thecomputing system's registers and/or memories into other data similarlyrepresented as physical quantities within the computing system'smemories, registers or other such information storage, transmission ordisplay devices.

The processes and functions presented herein are not inherently relatedto any particular computer, imager, output device or other apparatus.Embodiments of the invention described herein are not described withreference to any particular programming language, machine code, etc. Itwill be appreciated that a variety of programming languages, systems,protocols or hardware configurations may be used to implement theteachings of the embodiments of the invention as described herein.

Reference is made to FIG. 1, a conceptual illustration of components ofa system, in accordance with an embodiment of the invention. In someembodiments, components of a system that may be used in implementing anembodiment of the invention may include for example an imaging device100 such as for example a camera, such as for example a digital camera,a color digital imager or another suitable digital imaging device 100.In some embodiments, imaging device 100 may be or include for example adigital camera that may be linked to for example a computing device 102,such as for example a personal computer, work station, video gameconsole, personal digital assistant, local area network, wide areanetwork or other computing device 102 or system. One or both of imagingdevice 100 and computing device 102 may include for example a processor104, such as for example a CPU or other processor. In some embodiments,processor 104 may include digital signal processing capabilities. Insome embodiments, one or more of imaging device 100 and computing device102 may include a data storage unit such as for example a memory 105such as a random access memory, disc drive, flash memory or other massdata storage unit that may store for example image data or other data.In some embodiments, a system may include an output device 106 such asfor example a display, such as a computer screen, video or other screen,one or more speakers, or other output devices 106 that may for exampleissue an instruction or other message that may be seen, heard, orotherwise perceived by a user or subject.

In some embodiments, one or more of the components of a system of theinvention may be combined into fewer, or separated into a greater numberof devices. For example, imaging device 100 may in some embodimentsinclude or be connected to processor 104, memory 105 and output device106, and all of such components may be housed in a single unit.

In operation and in some embodiments of the invention, imaging device100 may capture an image of a figure 110. Processor 104 may calculatecertain location coordinates of a figure 110 in the captured image, andsuch coordinates may define for example a position of one or more limbs,extremities or other features of figure 110 as such features may appearin the image. Output device 106 or some other component may instruct auser or figure 110 to alter, vary or change a position of one or morelimbs, body parts, extremities or other features of figure 110. Imagingdevice 100 may capture another image of figure 110, and processor 104may calculate another set of location coordinates that may define aposition of figure 110 in such other image. Processor 104 may compareone or more of the location or position coordinates of figure 110, or apart of such figure 110, in the image that was captured for examplebefore an instruction was given, with the location or positioncoordinates of figure 110, or a part of such figure 110, in the imagethat was captured after the instruction was given. Based on for examplesuch comparison, one or more output devices 106 may signal a user as tothe compliance of figure 110 with the instruction to alter or change aposition. In some embodiments, an output device 106 may for examplesignal a user or other subject as to a non-compliance with theinstruction, may indicate the nature of the non-compliance, and maysuggest a correction of the position of figure 110 to achieve compliancewith the instruction.

In some embodiments an image of a figure 110 may be captured when suchimage and figure is lit by for example incandescent, fluorescent orother visible light, or light that is generally readily available fromfor example ambient indoor lighting systems. In some embodiments, animage of a figure 110 may be captured when figure 110 is captured inoutdoor light.

In some embodiments an instruction may be to add, pick up, discard orotherwise interact with one or more objects. In some embodiments, aninstruction may be to interact with an object of a particular color,shape, consistency or other characteristic.

In some embodiments, figure 110 may be or include for example a bodysuch as a human body or the body of some other animal or animate object.In some embodiments, a figure 110 may include a body and one or moreinanimate objects such as for example blocks, tools, components orassemblies. In some embodiments, a figure 110 may include one or moreinanimate objects that may be acted upon by a person or other user.

In some embodiments, processor 104 may process image data in a firstimage, and may separate or differentiate image data of a background fromimage data of figure 110. In some embodiments, imaging device 100 mayinitially or at some other time capture an image of a background withoutfigure 110. For example, in some embodiments, a system, by way of forexample, output device 106 or otherwise, may request that a figure 110be removed from an area to be imaged, and an image of the area may becaptured. The compliance of a figure 110 or user with such request maybe for example tested or confirmed by for example motion detectiontechniques such as for example, checking for movement in the area to beimaged, such as by using for example a continuous consecutive framesubtraction along with a low threshold on the absolute subtractionvalues. The suspected movement areas may be verified by a connectedcomponent criteria to distinguish between real regions of movement andnoisy isolated pixels. Other methods of confirming compliance with arequest for a figure to be absent from a background are possible.

In a period when for example a figure 110 is out of view of imagingdevice 100, or during some other period, a processor may capture one ormore images of a background and calculate statistics of a per pixelmean+variance image matrix of the background. In some embodiments,deviations from the statistically based mean+{acute over (α)} times avariance threshold criteria per pixel in the image once figure 110 hasentered the field of view of imaging device 100 may be used to determinethe presence of figure 110 in the view of a particular pixel. Anotherbackground removal technique may include calculating a matrix of medianvalues from a set of frames, and applying a threshold criteria toseparate foreground objects. An appropriate threshold may be calculatedusing for example an analysis of a histogram of pixel values of thedifference image. A typical difference image may result in a bi-modalhistogram, where the brighter modality represents the foreground objectsuch as a figure 110. In some embodiments, a threshold may be set as thegrey level that maximizes the perpendicular distance to a point on thehistogram from a line that connects or is tangent to the two modal'speaks. Where imaging device 100 is a color imager, this process may beperformed on the different color channels separately, and the differenceimage may be performed for example from the maximum intensity values perpixel of the difference images from the different color channels. Othermethods of background removal may be used.

Reference is made to FIG. 2A, an illustration of a binary filtered imageof a figure 110 following the differentiation of the foreground imagefrom for example a background. Morphological filters such as closing andopening may be used to for example smooth boundaries of figure 110, andto for example fill gaps that may have been caused by for exampleimperfect binarization effects. For example, an area opening process mayinclude a binary image labeling where connected components of the binaryimage are labeled in a unique manner, and labeled segments having anarea below a given threshold are filled in or erased. Other processesmay be used to unify or define an outline and area of a foreground orfigure 110.

In some embodiments, an imaging device 100 or one or more othercomponents of a system may center figure 110 within a field of view sothat for example, at least a head and arms, or other extensions desiredto be viewed are detected by imaging device 100. In some embodiments, asystem, an operator or a component may direct that a field of view ofimaging device 100 is to be centered around a largest non-backgroundobject in a field of view of imaging device 100, on the assumption thatfigure 110 is the largest object. Other methods of centering figure 110in a field of view are possible. In some embodiments, an instruction toa user to center figure 110 in a field of view may be passed throughoutput device 106. In some embodiments, a camera may be moved inresponse to a signal from for example processor 104, to center the fieldof view of imaging device 100 on figure 110. In some embodiments, afield of view may not be centered around a particular object or figure110, such as for example when there are two figures 110 whose positionsare being evaluated.

In some embodiments a pruned skeleton of figure 110 may be derived usingfor example a medial axis transform technique. For example, FIG. 2Billustrates a skeletonized image of a figure in accordance with anembodiment of the invention. A skeletonization of figure 110 may reducethe complexity of the classification of a pose of figure 110 byrepresenting figure 110 as a series of lines and curves. Apost-processing pruning step may be added to the skeletonizationprocess, and such pruning may get rid of outlying or non-continuousextensions of the skeleton. In some embodiments, a skeletonizationtechnique with built-in pruning of noisy artifacts, such as for examplethe Zhang-Suen technique, may be used. Other skeletonization processesare possible, and other methods of quantifying or reducing a complexityof a pose of figure 110 may be used.

From the skeletonized figure, major convex deficiencies may be extractedby subtracting the skeleton from the convex hull image of the skeleton.Area opening processes may also filter out small regions from the resultto further simplify the figure. For example, FIG. 2C illustrates convexhull deficiencies of the skeletonized figure 110. One or more of theconvex hull deficiencies may be assigned a distinctive color or otherlabel.

In some embodiments, a point representing an intersection of the arms orlateral axis of a figure 110 or skeleton of the figure 110, with a heador vertical axis of a figure 110 or skeleton of the figure 110, may bedesignated as a center of mass of figure 110. Other points or areas on afigure 110 may be designated as a center of mass. In some embodiments, acenter of mass may be identified by a convolution operation of theskeletonized image with a constant value kernel that sums up the numberof pixels in the skeleton. The convolved image may be applied with apredefined threshold value to find candidates for the center of masspoint since the intersection points of a lateral or arm-shoulder axisand a vertical or head-torso axis may have more pixels than the curvesor lines may have. In some embodiments, a conjecture of a center of massmay be confirmed as the desired center of mass by for exampledetermining that such point is the meeting point of four neighboringconvex hull deficiencies. Other methods of identifying or designating amass center of a figure 110 are possible.

A Euclidian distance from a mass center point of a figure 110 to acenter or mass center of one or more of the convex hull deficiencies maybe calculated. As is shown in FIG. 2D, distances and positions may becalculated from a center of mass of a figure 110 to a center of one oreach of the convex hull deficiency areas of figure 110. The convex hulldeficiencies positions may be designated by using for example quadrantsaround a center of mass of figure 110, and the quadrants may bereferenced by the X,Y coordinates of the center or center of mass of aparticular convex hull deficiency relative to the center of mass offigure 110. This location may be deducted from the sign of thedifferences in both X,Y directions calculated above to determine aposition of the center of the deficiency relative to the mass center offigure 110.

A further calculation may be made of an openness of a particular convexhull deficiency. As is shown in FIG. 2D, a measure of the openness of aparticular convex hull deficiency may be calculated by comparing the forexample total perimeter length of particular convex hull deficiency tothe portion of such perimeter that is included in a skeletonized figure.For example, and in some embodiments, when the openness value is 1, theentire convex hull deficiency may be enclosed within a skeletonizedimage. Openness may be used as an indication of whether an end or otherportion of an arm, leg or other extremity is touching a body. Othermeasures of openness may be used and other calculations of an extremityrelative to a body mass may be made. Other features of the figure may beextracted.

In some embodiments, distances of centers of convex hull deficienciesfrom a mass center of figure 110 may be normalized by a factor which mayfor example be determined from for example a bounding box of figure 110.Other normalization techniques may be used. Such normalization mayimpose an invariability to scale that may account for variability in thedistance between for example an imaging device 100 and figure 110, orbetween a size of a particular feature or extremity of figure 110.

In some embodiments, a vector of the extracted features such as thelocation coordinates of centers of convex hull deficiencies as arecollected in a first image, may be compared with corresponding locationcoordinates of centers of convex hull deficiencies in a second image. Insome embodiments, the vectors of the location coordinates may be used toclassify a pose of figure 110 by comparing or examining a Euclidiandistance between this vector and a set of pre-learned vectors which mayhave been collected from prior analysis of a designated pose or a posereferred to by an instruction. In some embodiments, similarities betweena derived vector of location coordinates and a pre-learned set ofcoordinates may be thresholded against minimum matching criteria todetermine compliance or the extent of compliance with an instruction.For example, in some embodiments, real and/or normalized distances andpositions of for example one or more location coordinates of for examplecenters or centers of mass of convex hull deficiencies relative to forexample a mass center of a figure may be compared to an estimate or oneor more samples of such real or normalized distances in respect of otherfigures as such other figures may have assumed a particular position.Such comparison may be used as an indication of a compliance of a figurewith an instruction to assume a particular position, or for example aspart of a determination as to a position of a figure. For example, asample or collection of various location coordinates of figures in oneor more positions may be collected in for example a data base. Thecollection may include for example location coordinates of sitting,standing, bending figures, and of figures in various other positions.Location coordinates of a figure may be collected and compared to suchsamples or collection of figures. A determination of the position of thesubject figure may be made on the basis of such comparison.

Reference is made to FIG. 2E, a proportion of convex hull deficiencyperimeters that are included in a skeleton of a figure in accordancewith an embodiment of the invention. In such figure, the perimeters ofconvex hull deficiencies that are not part of the skeleton of the figureare shown in crossed lines. In some embodiments, openness factors, suchas for example the number of closed loops of a skeletonized figure, orof a convex hull deficiency from an image that was captured before aninstruction, may be compared to openness factors of an image capturedafter an instruction. Such comparisons may indicate a change in aposition of one or more skeleton segments from one image to another.Other comparisons between location coordinates of parts of askeletonized image and of centers of convex hull deficiencies may bemade to determine a pose of figure 110, or the compliance of figure 110with an instruction. Other measures of similarity on the featuresvectors may be used.

In some embodiments, location coordinates may refer to a point or areaof an image relative to another point or area in the image, rather thanto an actual point in space. For example, a location coordinate mayrefer to a point or area in an image relative to for example a masscenter of a figure in the image. Location coordinates may be describedas relative to some other reference point in an image.

In some embodiments, a data base of pre-learned vectors that maycorrespond to a pose may be updated with vectors collected from aparticular user or FIG. 10. In some embodiments, non-parametricclustering techniques may be used to approximate representativecharacteristics of a particular pose. Furthermore, coordinates may becollected from more than one image such as for example from a series ofimages of figure 110, and a median of the collected coordinates may beconsidered in defining a position of figure 110, and in comparing theposition to a pre-learned or prior set of coordinates. In someembodiments, collections of vectors may be classified as examples offigures in a particular position, and a comparison of particular vectorsto the collections may estimate or determine the pose of a figure 110.In some embodiments, coordinates such as size, shape or color of variousobjects, such as balls, cubes, toys or instruments may be stored in forexample a data base linked to a system, and the collected coordinatesfrom for example a current image may be compared to the storedcoordinates to determine for example if the object in an image is arecognized or known to the data base. For example, in an embodiment of atoy, coordinates of one or more known shapes of for example animals,pictures or other objects that may be part of for example a game, may bestored in a data base, such that a system may indicate when suchrecognized object appears in an image.

In some embodiments, location coordinates may be collected over a seriesof frames, and a figure's compliance with an instruction may becalculated continuously, to note for example that a figure has changed aposition. In some embodiments, only a significant variation from a rangeof stored coordinates that are associated with a pose may elicit asignal of non-compliance with an instruction.

In some embodiments, an instruction may entail an introduction into animage of a colored object. For example, an instruction may be or include“Now pick up a red ball with your left hand”. Compliance with acolor-dependent instruction may be gauged by clustering for examplenon-background colors in an image that is captured prior to theinstruction, and repeating the color clustering in an image that iscaptured following the instruction. A comparison of image intensitiesand locations of the color clusters between the images, may be used indetermining compliance with an instruction. Similarly, a comparison ofone or more location coordinates in a pre-instruction andpost-instruction image may be combined with a location coordinate of acolor cluster to determine for example a proximity of a designated limbor extremity to a particular color cluster.

For example, the colors appearing in a foreground in a pre-instructionimage may be clustered in for example a non-parametric clusteringprocess that allows a large set of color pixels to be clustered. In someembodiments, a mean-shift algorithm may be used in such clustering.Other clustering methods may be used. In some embodiments, the colorsmay be extracted and the color vector may be recorded. The main colorsof the figure may be extracted using another clustering on the colorvectors found. A comparison may be made between the color clusters, andin some embodiments the locations of such clusters in an image capturedprior to an instruction, and the color clusters and their locations inan image captured after the instruction. Other methods of colorsegmentation may be used.

In some embodiments, a clustering procedure for colored objectrecognition may be performed on one or a series of images, where forexample the basis of pixel values in a colored portion of an image thatis for example newly introduced may be compared with for example coloredareas in a prior image. For example, a clustering may be performed on acolor space that includes for example Red, Green Blue, (other colorspaces can be used), where a result of for example a non-parametricclustering (such as mean-shift) may be a set of color vectors thatrepresent for example the major colors in the image. When performed on afew images these color vectors can be determined with a greater accuracyby performing for example a second color based clustering on coloredareas that had been clustered into for example broad cluster ranges. Insome embodiments, the location of for example a color cluster may bemapped relative to the location of for example other color clusters,skeleton hull deficiencies or the location coordinates of other parts offor example a figure.

When for example a new color or a new colored object appears in animage, a further clustering procedure may be performed that may detectthe presence of the new color or the new colored object and may identifya color of an object in the image. If for example no color vector fromthe former image or calculation is similar or close in space or locationto a color cluster in a prior or previous image, such color or coloredobject may be detected as a new color or new colored object. In someembodiments, a color clustering or other detection of a color or coloredobject may be undertaken with or without background subtraction.

If the class of instructions used for example is to show a known objectwith a certain shape or image on it to the imaging device 100, a patternrecognition algorithm may use a fast scale and rotation invarianttransform such as the Mellin transform based template matching.

In some embodiments, a signal may be issued to for example a user or afigure 110 to note compliance or non-compliance with an instruction. Forexample an output device 106 such as one or more speakers may employvoice synthesis instructions or taped messages to inform a user of hiscompliance or non-compliance with an instruction. In some embodiments aprocessor 104 may access a data base of parts of messages and assemble aresponse that matches the compliance or non-compliance with theinstruction. In some embodiments, a message may appear on a screen orother display to indicate compliance or non-compliance.

In some embodiments, a processor may diagnose a non-compliance, andinform a user of the particular aspect of a non-compliance along with asuggestion of how to correct the non-compliance. Other encouragements orinstructions may be transmitted or conveyed. For example, a message of“Raise your knees higher” or “That's good, now try it with your elbowsbent” may be conveyed to a user or figure 110. In some embodiments, aseries of instructions may create for example an exercise regimen for auser, a training session or other series of movements.

Reference is made to FIG. 3, a flow diagram of a method in accordancewith an embodiment of the invention. In block 300, a calculation of oneor more position coordinates of a figure in an image may be performed.In some embodiments, the position coordinates may include for example amass center of a figure, and for example one or more locationcoordinates of for example a center, edge or other point of one or moreconvex deficiencies of a for example skeletonized image of a figure.Other location coordinates may be calculated and stored in for example amemory.

In block 302, a second calculation may be performed of locationcoordinates of the figure in a second image. In some embodiments, thelocation coordinates of the figure in the second image may be comparedwith the location coordinates of the figure in the first image, and thedifferences in such locations may be calculated.

In block 304, the location of particular coordinates in the second imageare compared to for example corresponding location coordinates in thefirst image, and the relative movement or change in position of thecoordinates may be calculated along with an expected position of suchcoordinates that would correspond to the position to be taken by afigure in compliance with an instruction to assume a defined position.For example a coordinate of a top of a head in a skeletonized image of afigure in a second image may be lower, either absolutely or relativelythan a coordinate of a mass center of a figure in a first image. Thelower coordinate for a top of a head may correspond to an instruction toa figure to perform a knee bend that was issued between the capture ofthe first image and the capture of the second image. In someembodiments, an output device may indicate compliance with aninstruction to the user or figure.

In some embodiments, a method of the invention may monitor a figure todetermine if the figure has altered a position. For example, in someembodiments, a method of the invention may capture a first image of forexample a bed-ridden or otherwise immobilized figure, as the figure mayfor example rest in a bed. A method of the invention may monitor themovement of the figure, and may issue an alert if the figure moves forexample a limb or extremity to an extent that may exceed a pre-definedthreshold.

In some embodiments, one or more location coordinates of a figure may becompared to for example one or more samples of location coordinates offigures in one or more positions. A determination of the position of thefigure in an image may be made by such comparison. In some embodiments,a collection or data base of ‘learned coordinates’ may be collected fromfor example a single user or a sample of more than one users, and suchsample or learned coordinates may be stored as corresponding to aparticular pose. Coordinates that may be collected later may be comparedto such learned poses to determine a compliance with an instruction or aprogress of a user. For example, a patient undergoing a course ofphysical therapy may be instructed to assume a position or perform anexercise. A therapist may perform the exercise and a system may recordthe coordinates of the therapist during the sample period. A patient maythen perform the same exercise, and an embodiment of the invention maycompare the patient's performance to that of the therapist, and maytrack the patient's performance.

In some embodiments, a system may refine or continuously learn toidentify a pose or position of a particular user. For example, in thecase where location coordinates or color coordinates of a particularpose or object are stored in for example a data base, a system maycollect coordinates of a particular user in a same position. A systemmay upon evaluating a position of a figure in an image, refine thestored coordinates by comparing them with the actual coordinatescollected from the image of the user in the same position. In someembodiments, an average of the stored and the new coordinates may becomepart of a new measure for determining a compliance of a user with theparticular pose. For example, a coefficient α which can be derived froma level of certainty of a classification of a position such as(1−α)*OLD+α*NEW when α=0.01. This will bias the classification vectortoward a better representation of that position for the specific subjectfor better future classification rate.

It will be appreciated by persons skilled in the art that embodiments ofthe invention are not limited by what has been particularly shown anddescribed hereinabove. Rather the scope of at least one embodiment ofthe invention is defined by the claims below.

1. A system comprising: an imaging device to capture, using visiblelight, first and second images, each including first and second figures;an output device to output instructions for a change in a position ofeach of said first and second figures; and a processor to compare, foreach of said figures, a first position in said first image to a secondposition in said second image and to determine compliance with theinstructions.
 2. The system as in claim 1, wherein said processorevaluates a location coordinate of each of said figures in each of saidimages.
 3. A system comprising: an imaging device to capture a pluralityof images using visible light, a first of said images including afigure; a processor; and an output device; wherein said processordetermines compliance with an instruction from said output device tointroduce an object into a second of said plurality of images, saidsecond image including said figure and said object.
 4. The system as inclaim 3 wherein said processor determines compliance with saidinstruction by comparing said first image to said second image.
 5. Thesystem as in claim 3, wherein said second image includes said object ina second position; said first image includes said figure and saidobject, said object in a first position; and said processor determinescompliance with said instruction to alter a position of said object. 6.The system as in claim 3, wherein said processor compares acharacteristic of said object in said second image with a characteristicof said object stored in a memory.
 7. The system as in claim 6, whereinsaid characteristic is selected from a group consisting of a shape ofsaid object, a color of said object and a consistency of said object. 8.The system as in claim 3, wherein said processor determines compliancewith said instruction to alter a proximity of said object relative tosaid figure.
 9. The system as in claim 3, wherein said processoridentifies said object by a color clustering of said second image.
 10. Asystem comprising an imaging device to capture an image using visiblelight, said image including an object and a figure; a processor; and anoutput device; wherein said processor determines compliance with aninstruction issued by said output device, said instruction for saidfigure to interact with said object.
 11. The system as in claim 10,wherein said image is a second image, and wherein said imaging devicecaptures a first image, and wherein said interacting is selected fromthe group consisting of adding said object to said second of saidplurality of images, picking up said object, and discarding said objectfrom said second image.